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        "author": "Sean P. Cornelius",
        "author_email": "spcornelius@gmail.com",
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            "Development Status :: 3 - Alpha",
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            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering :: Mathematics",
            "Topic :: Scientific/Engineering :: Physics",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "paramnet\n========\n\n``paramnet`` provides a convenience mixin, `Parametrized`, for creating subclasses of\nNetworkX's `Graph` (`DiGraph`) that have numeric parameters associated with nodes and\nedges (for example, a dynamical system on a network). It provides the following key features:\n\n* Simplified reads/writes for the parameter value for a specific node/edge, without navigating NetworkX's nested dict structure\n* Enforcing a well-defined node order on the graph, which in turn allows...\n* Extraction of all values of a given node (edge) parameter as a 1D (2D) `numpy` array\n\n\nExamples\n--------\nA parametrized network class can be created by subclassing from `Graph` or `DiGraph`\nusing the `Parametrized` mixin. The names of any node/edge parameters can be specified\nin the class definition:\n\n.. code:: python\n\n    >>> class MyGraph(Parametrized, Graph, node_params=['x'], edge_params=['y']):\n    ...    pass\n\n    G = MyGraph()\n..\n\nNote: As with all mixins, `Parametrized` should be inherited from *first* as above.\n\n`paramnet` automatically adds named fields for each declared parameter, supporting clean random\naccess (both read and write) by node or edge\n\n.. code:: python\n\n    >>> G.add_edge('a', 'b', y=3)\n    >>> G.x['a']\n    100\n    >>> G.x['a'] = 4\n    >>> G.y['a', 'b']\n    3\n    >>> G.y['a', 'b'] = 50\n..\n\nContrast this with the more cumbersome random access in base NetworkX (which can be used interchangeably if you wish):\n\n.. code:: python\n\n    >>> G.nodes['a']['x']\n    100\n    >>> G.nodes['a']['x'] = 4\n    >>> G.edges['a', 'b']['y']\n    3\n    >>> G.edges['a', 'b']['y'] = 50\n\n..\n\nWhat's more, `paramnet` maintains the order in which nodes were added, allowing index lookup:\n\n.. code:: python\n\n    >>> G.index('a')\n    0\n    >>> G.index('b')\n    1\n    >>> G.add_node('c')\n    >>> G.index('c')\n    2\n\n..\n\nBut that's not all. Because the nodes are ordered, we can get a well-defined representation of *all*\nvalues of a given node (edge) parameter at once as a vector (matrix). This is accomplished within `paramnet`\nby accessing the associated parameter attribute without square brackets:\n\n.. code:: python\n\n    >>> G.x\n    array([100, 100])\n    >>> G.y\n    array([[0., 3.],\n           [3., 0.]])\n\n    >>> G.A\n    array([[0., 1.],\n           [1., 0.]])\n..\n\nNote the special case for the weighted network adjacency matrix, which is automatically defined\nfor every graph through the field `A` (instead of `weight`), regardless of whether `weight` is\nsupplied in `edge_params`.\n\nUnder the hood, the parameter fields return View objects that wrap most `numpy` functionality,\nallowing vector arithmetic and other array operations\n\n.. code:: python\n\n    >>> G = MyGraph()\n    >>> G.add_nodes_from([(node, {'x': 5*node+1}) for node in range(5)])\n    >>> G.add_cycle(range(5), y=1)\n\n    # number of paths of length two between node pairs\n    >>> np.dot(G.A, G.A)\n    array([[0., 3., 1., 1., 3.],\n           [3., 0., 3., 1., 1.],\n           [1., 3., 0., 3., 1.],\n           [1., 1., 3., 0., 3.],\n           [3., 1., 1., 3., 0.]])\n\n    >>> G.x + 1\n    array([ 2,  7, 12, 17, 22])\n\n..\n\nDependencies\n------------\n* NetworkX (>= 2.0)\n* numpy\n\nLicense\n-------\n\n``paramnet`` is released under the MIT license. See LICENSE for details.\n\n",
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